mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2026-03-06 21:53:50 +01:00
d278bb46a2
- Support multi-turn conversations (OpenAI messages + ShareGPT formats) - Automatic assistant-only label masking via incremental tokenization - Use tokenizer.apply_chat_template() for proper special token handling - Add "Chat Template" option to the Data Format dropdown - Also accept instruction/output datasets (auto-converted to messages) - Validate chat template availability and dataset format upfront - Fix after_tokens[-1] IndexError when train_only_after is at end of prompt - Update docs
855 lines
44 KiB
Python
855 lines
44 KiB
Python
import os
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os.environ["WANDB_MODE"] = "offline"
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# os.environ["WANDB_DISABLED"] = "true"
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import json
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import math
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import random
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import shutil
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import sys
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import threading
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import time
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import traceback
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from datetime import datetime
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from pathlib import Path
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import gradio as gr
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from modules import shared, ui, utils
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from modules.evaluate import (
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calculate_perplexity,
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generate_markdown_table,
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save_past_evaluations
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)
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from modules.logging_colors import logger
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from modules.models import reload_model
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from modules.utils import natural_keys
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PARAMETERS = ["lora_name", "always_override", "all_linear", "q_proj_en", "v_proj_en", "k_proj_en", "o_proj_en", "gate_proj_en", "down_proj_en", "up_proj_en", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after", "stop_at_loss", "add_eos_token", "min_chars", "report_to"]
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WANT_INTERRUPT = False
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train_log = {}
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train_template = {}
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def create_ui():
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mu = shared.args.multi_user
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with gr.Tab("Training", elem_id="training-tab"):
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with gr.Tab('Train LoRA', elem_id='lora-train-tab'):
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tmp = gr.State('')
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with gr.Row():
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with gr.Column():
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gr.Markdown("[Tutorial](https://github.com/oobabooga/text-generation-webui/wiki/05-%E2%80%90-Training-Tab)")
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with gr.Row():
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copy_from = gr.Dropdown(label='Copy parameters from', value='None', choices=utils.get_available_loras(), elem_classes=['slim-dropdown'], interactive=not mu)
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ui.create_refresh_button(copy_from, lambda: None, lambda: {'choices': utils.get_available_loras()}, 'refresh-button', interactive=not mu)
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with gr.Row():
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with gr.Column(scale=5):
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lora_name = gr.Textbox(label='Name', info='The name of your new LoRA file')
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with gr.Column():
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always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name is the same, checking will replace the existing file, and unchecking will load and continue from it (the rank must be the same).', elem_classes=['no-background'])
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with gr.Accordion(label='Target Modules', open=False, elem_classes='tgw-accordion'):
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gr.Markdown("Selects which modules to target in training. Targeting more modules is closer to a full fine-tune at the cost of increased VRAM requirements and adapter size.\n\"Target all linear layers\" uses peft's `all-linear` option, which targets every `nn.Linear` layer except `lm_head` and works for any model architecture. Uncheck it to manually select individual projection modules below.")
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all_linear = gr.Checkbox(label='Target all linear layers', value=True, info='Targets every nn.Linear layer except lm_head. Works for any model architecture.', elem_classes=['no-background'])
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with gr.Row():
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with gr.Column():
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q_proj_en = gr.Checkbox(label='Enable q_proj', value=True)
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with gr.Column():
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v_proj_en = gr.Checkbox(label='Enable v_proj', value=True)
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with gr.Column():
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k_proj_en = gr.Checkbox(label='Enable k_proj', value=False)
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with gr.Column():
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o_proj_en = gr.Checkbox(label='Enable o_proj', value=False)
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with gr.Column():
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gate_proj_en = gr.Checkbox(label='Enable gate_proj', value=False)
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with gr.Column():
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down_proj_en = gr.Checkbox(label='Enable down_proj', value=False)
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with gr.Column():
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up_proj_en = gr.Checkbox(label='Enable up_proj', value=False)
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with gr.Row():
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with gr.Column():
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lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='Also called dimension count. Higher values = larger file, more content control. Smaller values = smaller file, less control. Use 4 or 8 for style, 128 or 256 to teach, 1024+ for fine-detail on big data. More VRAM is needed for higher ranks.')
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lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.')
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batch_size = gr.Slider(label='Batch Size', value=128, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.')
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micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.')
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cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=4096, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.')
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with gr.Column():
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save_steps = gr.Number(label='Save every n steps', value=0, info='If above 0, a checkpoint of the LoRA will be saved every time this many steps pass.')
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epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.')
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learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='In scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.')
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with gr.Row():
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lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.', elem_classes=['slim-dropdown'])
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with gr.Accordion(label='Advanced Options', open=False, elem_classes='tgw-accordion'):
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with gr.Row():
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with gr.Column():
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lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.')
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stop_at_loss = gr.Slider(label='Stop at loss', minimum=0.0, maximum=3.0, step=0.1, value=0.00, info='The process will automatically stop once the desired loss value is reached. (reasonable numbers are 1.5-1.8)')
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with gr.Row():
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optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.', elem_classes=['slim-dropdown'])
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with gr.Column():
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warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate will be lower than normal. This helps the trainer prepare the model and precompute statistics to improve the quality of training after the start.')
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train_only_after = gr.Textbox(label='Train Only After', value='', info='Only consider text *after* this string in any given chunk for training. For Alpaca datasets, use "### Response:" to only train the response and ignore the input.')
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add_eos_token = gr.Checkbox(label='Add EOS token', value=False, info="Adds EOS token for each dataset item. In case of raw text, the EOS will be added at the Hard Cut")
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higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.')
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report_to = gr.Radio(label="Save detailed logs with", value="None", choices=["None", "wandb", "tensorboard"], interactive=True)
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with gr.Column():
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with gr.Tab(label='Formatted Dataset'):
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with gr.Row():
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format = gr.Dropdown(choices=['None', 'Chat Template'] + [x for x in utils.get_datasets('user_data/training/formats', 'json') if x != 'None'], value='None', label='Data Format', info='The format file used to decide how to format the dataset input. "Chat Template" uses the model\'s built-in chat template via apply_chat_template().', elem_classes=['slim-dropdown'], interactive=not mu)
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ui.create_refresh_button(format, lambda: None, lambda: {'choices': ['None', 'Chat Template'] + [x for x in utils.get_datasets('user_data/training/formats', 'json') if x != 'None']}, 'refresh-button', interactive=not mu)
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with gr.Row():
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dataset = gr.Dropdown(choices=utils.get_datasets('user_data/training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.', elem_classes=['slim-dropdown'], interactive=not mu)
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ui.create_refresh_button(dataset, lambda: None, lambda: {'choices': utils.get_datasets('user_data/training/datasets', 'json')}, 'refresh-button', interactive=not mu)
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with gr.Row():
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eval_dataset = gr.Dropdown(choices=utils.get_datasets('user_data/training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.', elem_classes=['slim-dropdown'], interactive=not mu)
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ui.create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': utils.get_datasets('user_data/training/datasets', 'json')}, 'refresh-button', interactive=not mu)
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eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.')
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with gr.Tab(label="Raw text file"):
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with gr.Row():
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raw_text_file = gr.Dropdown(choices=utils.get_datasets('user_data/training/datasets', 'txt'), value='None', label='Text file', info='The raw text file to use for training.', elem_classes=['slim-dropdown'], interactive=not mu)
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ui.create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': utils.get_datasets('user_data/training/datasets', 'txt')}, 'refresh-button', interactive=not mu)
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hard_cut_string = gr.Textbox(label='Hard Cut String', value='\\n\\n\\n', info='String that indicates a hard cut between text parts. Helps prevent unwanted overlap.')
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min_chars = gr.Number(label='Ignore small blocks', value=0, info='Ignore Hard Cut blocks that have less or equal characters than this number')
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with gr.Row():
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start_button = gr.Button("Start LoRA Training", variant='primary', interactive=not mu)
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stop_button = gr.Button("Interrupt", interactive=not mu)
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output = gr.Markdown(value="Ready")
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with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'):
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with gr.Row():
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with gr.Column():
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models = gr.Dropdown(utils.get_available_models(), label='Models', multiselect=True, interactive=not mu)
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evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + utils.get_datasets('user_data/training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under user_data/training/datasets.', interactive=not mu)
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with gr.Row():
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with gr.Column():
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stride_length = gr.Slider(label='Stride', minimum=0, maximum=32768, value=512, step=256, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.')
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with gr.Column():
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max_length = gr.Number(label='max_length', precision=0, step=256, value=0, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.')
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with gr.Row():
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start_current_evaluation = gr.Button("Evaluate loaded model", interactive=not mu)
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start_evaluation = gr.Button("Evaluate selected models", interactive=not mu)
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stop_evaluation = gr.Button("Interrupt", interactive=not mu)
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with gr.Column():
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evaluation_log = gr.Markdown(value='')
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evaluation_table = gr.Dataframe(value=generate_markdown_table(), interactive=True)
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with gr.Row():
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save_comments = gr.Button('Save comments', elem_classes="small-button", interactive=not mu)
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refresh_table = gr.Button('Refresh the table', elem_classes="small-button", interactive=not mu)
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# Training events
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all_params = [lora_name, always_override, all_linear, q_proj_en, v_proj_en, k_proj_en, o_proj_en, gate_proj_en, down_proj_en, up_proj_en, save_steps, micro_batch_size, batch_size, epochs, learning_rate, lr_scheduler_type, lora_rank, lora_alpha, lora_dropout, cutoff_len, dataset, eval_dataset, format, eval_steps, raw_text_file, higher_rank_limit, warmup_steps, optimizer, hard_cut_string, train_only_after, stop_at_loss, add_eos_token, min_chars, report_to]
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copy_from.change(do_copy_params, [copy_from] + all_params, all_params)
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start_button.click(do_train, all_params, output)
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stop_button.click(do_interrupt, None, None, queue=False)
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higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha])
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# Evaluation events. For some reason, the interrupt event
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# doesn't work with the .then() syntax, so I write them one
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# by one in this ugly but functional way.
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ev = start_evaluation.click(calculate_perplexity, [models, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
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ev.then(generate_markdown_table, None, evaluation_table, show_progress=False)
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ev_cur = start_current_evaluation.click(
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lambda: ['current model'], None, tmp).then(
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calculate_perplexity, [tmp, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False)
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ev_cur.then(generate_markdown_table, None, evaluation_table, show_progress=False)
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stop_evaluation.click(None, None, None, cancels=[ev, ev_cur], queue=False)
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refresh_table.click(generate_markdown_table, None, evaluation_table, show_progress=True)
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save_comments.click(
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save_past_evaluations, evaluation_table, None).then(
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lambda: "Comments saved.", None, evaluation_log, show_progress=False)
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def do_interrupt():
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global WANT_INTERRUPT
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WANT_INTERRUPT = True
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def do_copy_params(lora_name: str, *args):
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f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json"
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if Path(f_name).is_file():
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with open(f_name, 'r', encoding='utf-8') as format_file:
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params: dict[str, str] = json.load(format_file)
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else:
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params = {}
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result = list()
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for i in range(0, len(PARAMETERS)):
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key = PARAMETERS[i]
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if key in params:
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result.append(params[key])
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else:
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result.append(args[i])
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return result
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def change_rank_limit(use_higher_ranks: bool):
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mult = 2 if use_higher_ranks else 1
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return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"}
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def clean_path(base_path: str, path: str):
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"""Strips unusual symbols and forcibly builds a path as relative to the intended directory."""
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path = path.replace('\\', '/').replace('..', '_')
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if base_path is None:
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return path
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return f'{Path(base_path).absolute()}/{path}'
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def backup_adapter(input_folder):
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# Get the creation date of the adapter file (safetensors or bin)
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try:
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adapter_file = Path(f"{input_folder}/adapter_model.safetensors")
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if not adapter_file.is_file():
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adapter_file = Path(f"{input_folder}/adapter_model.bin")
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if adapter_file.is_file():
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logger.info("Backing up existing LoRA adapter")
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creation_date = datetime.fromtimestamp(adapter_file.stat().st_ctime)
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creation_date_str = creation_date.strftime("Backup-%Y-%m-%d")
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# Create the new subfolder
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subfolder_path = Path(f"{input_folder}/{creation_date_str}")
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subfolder_path.mkdir(parents=True, exist_ok=True)
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# Check if the file already exists in the subfolder
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backup_adapter_file = subfolder_path / adapter_file.name
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if backup_adapter_file.is_file():
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print(" - Backup already exists. Skipping backup process.")
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return
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# Copy existing files to the new subfolder
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existing_files = Path(input_folder).iterdir()
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for file in existing_files:
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if file.is_file():
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shutil.copy2(file, subfolder_path)
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except Exception as e:
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print("An error occurred in backup_adapter:", str(e))
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def calc_trainable_parameters(model):
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trainable_params = 0
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all_param = 0
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for _, param in model.named_parameters():
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num_params = param.numel()
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# if using DS Zero 3 and the weights are initialized empty
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if num_params == 0 and hasattr(param, "ds_numel"):
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num_params = param.ds_numel
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all_param += num_params
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if param.requires_grad:
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trainable_params += num_params
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return trainable_params, all_param
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def do_train(lora_name: str, always_override: bool, all_linear: bool, q_proj_en: bool, v_proj_en: bool, k_proj_en: bool, o_proj_en: bool, gate_proj_en: bool, down_proj_en: bool, up_proj_en: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float, add_eos_token: bool, min_chars: int, report_to: str):
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import torch
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import transformers
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from datasets import Dataset, load_dataset
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from peft import (
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LoraConfig,
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get_peft_model,
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prepare_model_for_kbit_training,
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set_peft_model_state_dict
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)
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global WANT_INTERRUPT
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WANT_INTERRUPT = False
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# == Input validation / processing ==
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yield "Preparing the input..."
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lora_file_path = clean_path(None, lora_name)
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if lora_file_path.strip() == '':
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yield "Missing or invalid LoRA file name input."
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return
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lora_file_path = f"{Path(shared.args.lora_dir)}/{lora_file_path}"
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actual_lr = float(learning_rate)
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model_type = type(shared.model).__name__
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if model_type == "PeftModelForCausalLM":
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if len(shared.lora_names) > 0:
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yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
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logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
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else:
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yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
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logger.warning("Model ID not matched due to LoRA loading. Consider reloading base model.")
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time.sleep(5)
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if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
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yield "Cannot input zeroes."
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return
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gradient_accumulation_steps = batch_size // micro_batch_size
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if shared.tokenizer.pad_token_id is None:
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shared.tokenizer.pad_token_id = shared.tokenizer.eos_token_id
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shared.tokenizer.padding_side = "left"
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def list_target_modules():
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if all_linear:
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return "all-linear"
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target_mods = [f"{name}_proj" for name, enabled in {
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"q": q_proj_en, "k": k_proj_en, "v": v_proj_en, "o": o_proj_en,
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"gate": gate_proj_en, "down": down_proj_en, "up": up_proj_en,
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}.items() if enabled]
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return target_mods
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def encode(text, add_bos_token):
|
|
result = shared.tokenizer.encode(text, truncation=True, max_length=cutoff_len)
|
|
# Check if the first two tokens are BOS
|
|
if len(result) >= 2 and result[:2] == [shared.tokenizer.bos_token_id, shared.tokenizer.bos_token_id]:
|
|
result = result[1:]
|
|
|
|
if not add_bos_token and result[0] == shared.tokenizer.bos_token_id:
|
|
result = result[1:]
|
|
return result
|
|
|
|
def tokenize(prompt, append_eos_token=False):
|
|
|
|
if train_only_after == '' or train_only_after not in prompt:
|
|
input_ids = encode(prompt, True)
|
|
|
|
if append_eos_token and input_ids[-1] != shared.tokenizer.eos_token_id and len(input_ids) < cutoff_len:
|
|
input_ids.append(shared.tokenizer.eos_token_id)
|
|
|
|
pad_len = cutoff_len - len(input_ids)
|
|
labels = [-100] * pad_len + list(input_ids)
|
|
input_ids = [shared.tokenizer.pad_token_id] * pad_len + input_ids
|
|
|
|
else:
|
|
ind = prompt.index(train_only_after) + len(train_only_after)
|
|
before_tokens = encode(prompt[:ind], True)
|
|
after_tokens = encode(prompt[ind:], False)
|
|
|
|
if append_eos_token and len(after_tokens) > 0 and after_tokens[-1] != shared.tokenizer.eos_token_id:
|
|
after_tokens.append(shared.tokenizer.eos_token_id)
|
|
|
|
full_length = len(after_tokens) + len(before_tokens)
|
|
if full_length > cutoff_len:
|
|
after_tokens = after_tokens[:cutoff_len - len(before_tokens)]
|
|
else:
|
|
before_tokens = [shared.tokenizer.pad_token_id] * (cutoff_len - full_length) + before_tokens
|
|
|
|
input_ids = before_tokens + after_tokens
|
|
labels = [-100] * len(before_tokens) + list(after_tokens)
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"labels": labels,
|
|
"attention_mask": [0 if t == shared.tokenizer.pad_token_id else 1 for t in input_ids],
|
|
}
|
|
|
|
def normalize_messages(data_point):
|
|
"""Convert a dataset row to OpenAI messages format for apply_chat_template()."""
|
|
if "messages" in data_point:
|
|
return data_point["messages"]
|
|
|
|
if "conversations" in data_point:
|
|
role_map = {"human": "user", "gpt": "assistant"}
|
|
return [
|
|
{"role": role_map.get(turn.get("from", ""), turn.get("from", "")), "content": turn["value"]}
|
|
for turn in data_point["conversations"]
|
|
]
|
|
|
|
if "instruction" in data_point and "output" in data_point:
|
|
messages = []
|
|
if data_point.get("system", "").strip():
|
|
messages.append({"role": "system", "content": data_point["system"]})
|
|
messages.append({"role": "user", "content": data_point["instruction"]})
|
|
messages.append({"role": "assistant", "content": data_point["output"]})
|
|
return messages
|
|
|
|
raise RuntimeError(
|
|
f'Dataset row must contain "messages", "conversations", or "instruction"/"output" keys. '
|
|
f'Found: {list(data_point.keys())}'
|
|
)
|
|
|
|
def tokenize_conversation(data_point):
|
|
"""Tokenize using apply_chat_template() with assistant-only label masking."""
|
|
messages = normalize_messages(data_point)
|
|
full_ids = shared.tokenizer.apply_chat_template(messages, tokenize=True)
|
|
|
|
# Build labels: -100 for everything, then unmask assistant turns.
|
|
# This assumes apply_chat_template(messages[:i]) is a token-for-token
|
|
# prefix of apply_chat_template(messages[:i+1]), which holds for all
|
|
# standard chat templates (Llama, ChatML, Mistral, etc.).
|
|
labels = [-100] * len(full_ids)
|
|
for i, msg in enumerate(messages):
|
|
if msg["role"] == "assistant":
|
|
# Tokens up to where this assistant turn starts
|
|
header_ids = shared.tokenizer.apply_chat_template(
|
|
messages[:i], tokenize=True, add_generation_prompt=True
|
|
)
|
|
# Tokens through end of this assistant turn
|
|
through_ids = shared.tokenizer.apply_chat_template(
|
|
messages[:i + 1], tokenize=True
|
|
)
|
|
# Unmask assistant tokens
|
|
start = len(header_ids)
|
|
end = min(len(through_ids), len(full_ids))
|
|
labels[start:end] = full_ids[start:end]
|
|
|
|
# Truncate from the right: keeps the system prompt and early turns
|
|
if len(full_ids) > cutoff_len:
|
|
full_ids = full_ids[:cutoff_len]
|
|
labels = labels[:cutoff_len]
|
|
|
|
# Left-pad to cutoff_len
|
|
pad_len = cutoff_len - len(full_ids)
|
|
attention_mask = [0] * pad_len + [1] * len(full_ids)
|
|
labels = [-100] * pad_len + labels
|
|
input_ids = [shared.tokenizer.pad_token_id] * pad_len + full_ids
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"labels": labels,
|
|
"attention_mask": attention_mask,
|
|
}
|
|
|
|
train_template.clear()
|
|
|
|
# == Prep the dataset, format, etc ==
|
|
if raw_text_file not in ['None', '']:
|
|
train_template["template_type"] = "raw_text"
|
|
logger.info("Loading raw text file dataset")
|
|
fullpath = clean_path('user_data/training/datasets', f'{raw_text_file}')
|
|
fullpath = Path(fullpath)
|
|
if fullpath.is_dir():
|
|
logger.info('Training path directory {}'.format(raw_text_file))
|
|
raw_text = ""
|
|
file_paths = sorted(fullpath.glob('*.txt'), key=lambda path: natural_keys(path.name))
|
|
for file_path in file_paths:
|
|
if file_path.is_file():
|
|
with file_path.open('r', encoding='utf-8') as file:
|
|
raw_text += file.read().replace('\r', '')
|
|
|
|
logger.info(f"Loaded training file: {file_path.name}")
|
|
else:
|
|
with open(clean_path('user_data/training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
|
|
raw_text = file.read().replace('\r', '')
|
|
|
|
cut_string = hard_cut_string.replace('\\n', '\n')
|
|
eos_added = 0
|
|
all_tokens = []
|
|
for text_part in raw_text.split(cut_string):
|
|
if len(text_part.strip()) <= min_chars:
|
|
continue
|
|
|
|
tokens = shared.tokenizer.encode(text_part)
|
|
if add_eos_token:
|
|
tokens.append(shared.tokenizer.eos_token_id)
|
|
eos_added += 1
|
|
|
|
all_tokens.extend(tokens)
|
|
|
|
if eos_added > 0:
|
|
print(f"EOS added to {eos_added} text blocks")
|
|
|
|
del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM
|
|
|
|
# Concatenate-and-split: non-overlapping chunks of exactly cutoff_len
|
|
num_chunks = len(all_tokens) // cutoff_len
|
|
if num_chunks == 0:
|
|
yield "Error: text is too short to fill even one chunk of the given cutoff length."
|
|
return
|
|
|
|
train_data = Dataset.from_list([
|
|
{
|
|
"input_ids": all_tokens[i * cutoff_len:(i + 1) * cutoff_len],
|
|
"labels": all_tokens[i * cutoff_len:(i + 1) * cutoff_len],
|
|
"attention_mask": [1] * cutoff_len,
|
|
}
|
|
for i in range(num_chunks)
|
|
])
|
|
del all_tokens
|
|
eval_data = None
|
|
else:
|
|
if dataset in ['None', '']:
|
|
yield "Missing dataset choice input, cannot continue."
|
|
return
|
|
|
|
if format in ['None', '']:
|
|
yield "Missing format choice input, cannot continue."
|
|
return
|
|
|
|
if format == 'Chat Template':
|
|
# Use the model's built-in chat template via apply_chat_template()
|
|
if not getattr(shared.tokenizer, 'chat_template', None):
|
|
yield "Error: this model's tokenizer does not have a chat template. Use a format file instead, or load an instruct/chat model."
|
|
return
|
|
|
|
train_template["template_type"] = "chat_template"
|
|
|
|
logger.info("Loading JSON dataset with Chat Template format")
|
|
data = load_dataset("json", data_files=clean_path('user_data/training/datasets', f'{dataset}.json'))
|
|
|
|
# Validate the first row
|
|
try:
|
|
normalize_messages(data['train'][0])
|
|
except (RuntimeError, KeyError, IndexError) as e:
|
|
yield f"Error: {e}"
|
|
return
|
|
|
|
train_data = data['train'].map(
|
|
tokenize_conversation,
|
|
remove_columns=data['train'].column_names,
|
|
new_fingerprint='%030x' % random.randrange(16**30)
|
|
)
|
|
|
|
if eval_dataset == 'None':
|
|
eval_data = None
|
|
else:
|
|
eval_data = load_dataset("json", data_files=clean_path('user_data/training/datasets', f'{eval_dataset}.json'))
|
|
eval_data = eval_data['train'].map(
|
|
tokenize_conversation,
|
|
remove_columns=eval_data['train'].column_names,
|
|
new_fingerprint='%030x' % random.randrange(16**30)
|
|
)
|
|
else:
|
|
# Use format file for prompt generation
|
|
train_template["template_type"] = "dataset"
|
|
|
|
with open(clean_path('user_data/training/formats', f'{format}.json'), 'r', encoding='utf-8-sig') as formatFile:
|
|
format_data: dict[str, str] = json.load(formatFile)
|
|
|
|
# == store training prompt ==
|
|
for _, value in format_data.items():
|
|
prompt_key = f"template_{len(train_template)}"
|
|
train_template[prompt_key] = value
|
|
|
|
def generate_prompt(data_point: dict[str, str]):
|
|
for options, data in format_data.items():
|
|
if set(options.split(',')) == set(x[0] for x in data_point.items() if (type(x[1]) is str and len(x[1].strip()) > 0)):
|
|
for key, val in data_point.items():
|
|
if type(val) is str:
|
|
data = data.replace(f'%{key}%', val)
|
|
return data
|
|
raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"')
|
|
|
|
def generate_and_tokenize_prompt(data_point):
|
|
prompt = generate_prompt(data_point)
|
|
return tokenize(prompt, add_eos_token)
|
|
|
|
logger.info("Loading JSON datasets")
|
|
data = load_dataset("json", data_files=clean_path('user_data/training/datasets', f'{dataset}.json'))
|
|
train_data = data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
|
|
|
|
if eval_dataset == 'None':
|
|
eval_data = None
|
|
else:
|
|
eval_data = load_dataset("json", data_files=clean_path('user_data/training/datasets', f'{eval_dataset}.json'))
|
|
eval_data = eval_data['train'].map(generate_and_tokenize_prompt, new_fingerprint='%030x' % random.randrange(16**30))
|
|
|
|
# == We MUST reload model if it went through any previous training, even failed one ==
|
|
if shared.model_dirty_from_training:
|
|
selected_model = shared.model_name
|
|
if selected_model:
|
|
print("\033[1;31;1m(Model has been modified by previous training, it needs to be reloaded...)\033[0;37;0m")
|
|
try:
|
|
yield f"Reloading {selected_model}..."
|
|
reload_model()
|
|
if shared.model is not None:
|
|
print("Model reloaded OK, continue with training.")
|
|
else:
|
|
return f"Failed to load {selected_model}."
|
|
except Exception:
|
|
exc = traceback.format_exc()
|
|
logger.error('Failed to reload the model.')
|
|
print(exc)
|
|
return exc.replace('\n', '\n\n')
|
|
|
|
# == Start prepping the model itself ==
|
|
if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
|
|
logger.info("Getting model ready")
|
|
if 'quantization_config' in shared.model.config.to_dict():
|
|
prepare_model_for_kbit_training(shared.model)
|
|
|
|
# base model is now frozen and should not be reused for any other LoRA training than this one
|
|
shared.model_dirty_from_training = True
|
|
|
|
logger.info("Preparing for training")
|
|
target_modules = list_target_modules()
|
|
if not target_modules:
|
|
yield "No target modules selected. Enable at least one module or check 'Target all linear layers'."
|
|
return
|
|
|
|
config = LoraConfig(
|
|
r=lora_rank,
|
|
lora_alpha=lora_alpha,
|
|
target_modules=target_modules,
|
|
lora_dropout=lora_dropout,
|
|
bias="none",
|
|
task_type="CAUSAL_LM"
|
|
)
|
|
|
|
# == Backup the existing adapter ==
|
|
if not always_override:
|
|
backup_adapter(lora_file_path)
|
|
|
|
# == get model trainable params
|
|
model_trainable_params, model_all_params = calc_trainable_parameters(shared.model)
|
|
|
|
try:
|
|
logger.info("Creating LoRA model")
|
|
lora_model = get_peft_model(shared.model, config)
|
|
if not always_override:
|
|
safetensors_path = Path(f"{lora_file_path}/adapter_model.safetensors")
|
|
bin_path = Path(f"{lora_file_path}/adapter_model.bin")
|
|
if safetensors_path.is_file():
|
|
logger.info("Loading existing LoRA data (safetensors)")
|
|
from safetensors.torch import load_file
|
|
state_dict_peft = load_file(str(safetensors_path))
|
|
set_peft_model_state_dict(lora_model, state_dict_peft)
|
|
elif bin_path.is_file():
|
|
logger.info("Loading existing LoRA data (bin)")
|
|
state_dict_peft = torch.load(str(bin_path), weights_only=True)
|
|
set_peft_model_state_dict(lora_model, state_dict_peft)
|
|
except Exception:
|
|
yield traceback.format_exc().replace('\n', '\n\n')
|
|
return
|
|
|
|
class Tracked():
|
|
def __init__(self):
|
|
self.current_steps = 0
|
|
self.max_steps = 0
|
|
self.did_save = False
|
|
|
|
tracked = Tracked()
|
|
actual_save_steps = math.ceil(save_steps / gradient_accumulation_steps)
|
|
|
|
class Callbacks(transformers.TrainerCallback):
|
|
def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
|
|
tracked.current_steps = state.global_step * gradient_accumulation_steps
|
|
tracked.max_steps = state.max_steps * gradient_accumulation_steps
|
|
if WANT_INTERRUPT:
|
|
control.should_epoch_stop = True
|
|
control.should_training_stop = True
|
|
elif state.global_step > 0 and actual_save_steps > 0 and state.global_step % actual_save_steps == 0:
|
|
lora_model.save_pretrained(f"{lora_file_path}/checkpoint-{tracked.current_steps}/")
|
|
# Save log
|
|
with open(f"{lora_file_path}/checkpoint-{tracked.current_steps}/training_log.json", 'w', encoding='utf-8') as file:
|
|
json.dump(train_log, file, indent=2)
|
|
# == Save training prompt ==
|
|
with open(f"{lora_file_path}/checkpoint-{tracked.current_steps}/training_prompt.json", 'w', encoding='utf-8') as file:
|
|
json.dump(train_template, file, indent=2)
|
|
|
|
def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
|
|
tracked.current_steps += 1
|
|
if WANT_INTERRUPT:
|
|
control.should_epoch_stop = True
|
|
control.should_training_stop = True
|
|
|
|
def on_log(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, logs, **kwargs):
|
|
train_log.update(logs)
|
|
train_log.update({"current_steps": tracked.current_steps})
|
|
if WANT_INTERRUPT:
|
|
print("\033[1;31;1mInterrupted by user\033[0;37;0m")
|
|
|
|
print(f"\033[1;30;40mStep: {tracked.current_steps} \033[0;37;0m", end='')
|
|
if 'loss' in logs:
|
|
loss = float(logs['loss'])
|
|
if loss <= stop_at_loss:
|
|
control.should_epoch_stop = True
|
|
control.should_training_stop = True
|
|
print(f"\033[1;31;1mStop Loss {stop_at_loss} reached.\033[0;37;0m")
|
|
|
|
# Fix training for mixed precision models
|
|
for param in shared.model.parameters():
|
|
if param.requires_grad:
|
|
param.data = param.data.float()
|
|
|
|
lora_model.config.use_cache = False
|
|
|
|
if sys.platform != "win32":
|
|
lora_model = torch.compile(lora_model)
|
|
|
|
def collate_fn(batch):
|
|
input_ids = torch.stack([torch.as_tensor(item['input_ids']) for item in batch])
|
|
labels = torch.stack([torch.as_tensor(item['labels']) for item in batch])
|
|
attention_mask = torch.stack([torch.as_tensor(item['attention_mask']) for item in batch])
|
|
return {
|
|
'input_ids': input_ids,
|
|
'labels': labels,
|
|
'attention_mask': attention_mask,
|
|
}
|
|
|
|
trainer = transformers.Trainer(
|
|
model=lora_model,
|
|
train_dataset=train_data,
|
|
eval_dataset=eval_data,
|
|
args=transformers.TrainingArguments(
|
|
report_to=report_to if report_to != "None" else "none",
|
|
per_device_train_batch_size=micro_batch_size,
|
|
gradient_accumulation_steps=gradient_accumulation_steps,
|
|
warmup_steps=math.ceil(warmup_steps / gradient_accumulation_steps),
|
|
num_train_epochs=epochs,
|
|
learning_rate=actual_lr,
|
|
fp16=False if shared.args.cpu or shared.args.bf16 else True,
|
|
bf16=shared.args.bf16,
|
|
optim=optimizer,
|
|
logging_steps=2 if stop_at_loss > 0 else 5,
|
|
eval_strategy="steps" if eval_data is not None else "no",
|
|
eval_steps=math.ceil(eval_steps / gradient_accumulation_steps) if eval_data is not None else None,
|
|
save_strategy="steps" if eval_data is not None else "no",
|
|
output_dir=lora_file_path,
|
|
lr_scheduler_type=lr_scheduler_type,
|
|
load_best_model_at_end=eval_data is not None,
|
|
# TODO: Enable multi-device support
|
|
ddp_find_unused_parameters=None,
|
|
use_cpu=shared.args.cpu,
|
|
remove_unused_columns=False,
|
|
),
|
|
data_collator=collate_fn,
|
|
callbacks=list([Callbacks()])
|
|
)
|
|
|
|
# == Save parameters for reuse ==
|
|
with open(f"{lora_file_path}/training_parameters.json", 'w', encoding='utf-8') as file:
|
|
vars = locals()
|
|
json.dump({x: vars[x] for x in PARAMETERS}, file, indent=2)
|
|
|
|
# == Save training prompt ==
|
|
with open(f"{lora_file_path}/training_prompt.json", 'w', encoding='utf-8') as file:
|
|
json.dump(train_template, file, indent=2)
|
|
|
|
# == Main run and monitor loop ==
|
|
logger.info("Starting training")
|
|
yield "Starting..."
|
|
|
|
lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
|
|
|
|
if target_modules == "all-linear":
|
|
projections_string = "all-linear"
|
|
else:
|
|
projections_string = ", ".join([projection.replace("_proj", "") for projection in target_modules])
|
|
|
|
print(f"Training '{model_type}' model using ({projections_string}) projections")
|
|
|
|
if lora_all_param > 0:
|
|
print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
|
|
|
|
train_log.update({"base_model_name": shared.model_name})
|
|
train_log.update({"base_model_class": shared.model.__class__.__name__})
|
|
train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})
|
|
train_log.update({"base_loaded_in_8bit": getattr(lora_model, "is_loaded_in_8bit", False)})
|
|
train_log.update({"projections": projections_string})
|
|
|
|
if stop_at_loss > 0:
|
|
print(f"Monitoring loss \033[1;31;1m(Auto-Stop at: {stop_at_loss})\033[0;37;0m")
|
|
|
|
if WANT_INTERRUPT:
|
|
yield "Interrupted before start."
|
|
return
|
|
|
|
def log_train_dataset(trainer):
|
|
decoded_entries = []
|
|
# Try to decode the entries and write the log file
|
|
try:
|
|
# Iterate over the first 10 elements in the dataset (or fewer if there are less than 10)
|
|
for i in range(min(10, len(trainer.train_dataset))):
|
|
decoded_text = shared.tokenizer.decode(trainer.train_dataset[i]['input_ids'])
|
|
decoded_entries.append({"value": decoded_text})
|
|
|
|
# Write the log file
|
|
Path('user_data/logs').mkdir(exist_ok=True)
|
|
with open(Path('user_data/logs/train_dataset_sample.json'), 'w') as json_file:
|
|
json.dump(decoded_entries, json_file, indent=4)
|
|
|
|
logger.info("Log file 'train_dataset_sample.json' created in the 'user_data/logs' directory.")
|
|
except Exception as e:
|
|
logger.error(f"Failed to create log file due to error: {e}")
|
|
|
|
def threaded_run():
|
|
log_train_dataset(trainer)
|
|
trainer.train()
|
|
# Note: save in the thread in case the gradio thread breaks (eg browser closed)
|
|
lora_model.save_pretrained(lora_file_path)
|
|
tracked.did_save = True
|
|
logger.info("LoRA training run is completed and saved.")
|
|
# Save log
|
|
with open(f"{lora_file_path}/training_log.json", 'w', encoding='utf-8') as file:
|
|
json.dump(train_log, file, indent=2)
|
|
|
|
thread = threading.Thread(target=threaded_run)
|
|
thread.start()
|
|
last_step = 0
|
|
start_time = time.perf_counter()
|
|
|
|
while thread.is_alive():
|
|
time.sleep(0.5)
|
|
if WANT_INTERRUPT:
|
|
yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*"
|
|
|
|
elif tracked.current_steps != last_step:
|
|
last_step = tracked.current_steps
|
|
time_elapsed = time.perf_counter() - start_time
|
|
if time_elapsed <= 0:
|
|
timer_info = ""
|
|
total_time_estimate = 999
|
|
else:
|
|
its = tracked.current_steps / time_elapsed
|
|
if its > 1:
|
|
timer_info = f"`{its:.2f}` it/s"
|
|
else:
|
|
timer_info = f"`{1.0/its:.2f}` s/it"
|
|
|
|
total_time_estimate = (1.0 / its) * (tracked.max_steps)
|
|
|
|
yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining"
|
|
|
|
# Saving in the train thread might fail if an error occurs, so save here if so.
|
|
if not tracked.did_save:
|
|
logger.info("Training complete, saving")
|
|
lora_model.save_pretrained(lora_file_path)
|
|
|
|
if WANT_INTERRUPT:
|
|
logger.info("Training interrupted.")
|
|
yield f"Interrupted. Incomplete LoRA saved to `{lora_file_path}`."
|
|
else:
|
|
logger.info("Training complete!")
|
|
yield f"Done! LoRA saved to `{lora_file_path}`.\n\nBefore testing your new LoRA, make sure to first reload the model, as it is currently dirty from training."
|
|
|
|
|
|
def format_time(seconds: float):
|
|
if seconds < 120:
|
|
return f"`{seconds:.0f}` seconds"
|
|
|
|
minutes = seconds / 60
|
|
if minutes < 120:
|
|
return f"`{minutes:.0f}` minutes"
|
|
|
|
hours = minutes / 60
|
|
return f"`{hours:.0f}` hours"
|